Dawn Song
Berkeley EECS (CS)
Research focus: adversarial robustness in machine learning
针对 Dawn Song 的预审审核：准备旗舰级成果，置于 AI/安全边界：LLM-代理攻击、程序分析、隐私保护 ML、区块 ai 安全、鲁棒性，或对抗压力下的可解释性。
Who this advisor fits / 什么情况下适合你
- You want ML tied to real data and deployable problems (signal: adversarial robustness in machine learn… · Blockchain Technology Applications and…).
- You care about faculty-track outcomes; public alumni rows include verifiable faculty placements.
- Industry research or startup/translation paths appeal to you (meaningful share in public outcomes).
- 你想把 ML 接到真实数据与落地问题（当前信号：adversarial robustness in machine learn… · Blockchain Technology Applications and…）。
- 你在意学术教职/教职轨去向，公开校友里有可核对的教职案例。
- 工业研究或创业/转化路径对你有吸引力（公开去向里占比不低）。
What to watch for / 什么情况下要慎重
- You want pure theory ML with little domain data or cross-disciplinary collaboration.
- Faculty-only is your sole goal and you want zero industry/translation exposure.
- 你要的是纯理论 ML、几乎不碰领域数据或跨学科合作。
- 你把「教职为唯一目标」且完全不想碰工业/转化网络。
Strong public placement signal · 公开去向信号：强
Public evidence as of 2026-06-10
These are decision-support signals compiled from public evidence (faculty pages, publications, lab sites) to help you ask better questions — not a ranking, rating, or allegation about this advisor. / 以上为基于公开信息整理的择校参考，帮助你提出更好的问题，并非排名、评分或对该导师的指控。
Ask a verified 学长学姐 / 同校 .edu 认证点评
The thing applicants say only 师兄师姐 can tell you — current & former students of this lab, verified by their school .edu. Open the full dossier to read or add a verified note.